Name entity recognizer for odia using conditional random fields

Abstract

Name Entity Recognition (NER) is a process of information extraction that seeks to locate atomic elements in text and classify them into predefined categories such as the name of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. In this paper, we present an Odia Named Entity Recognizer which will be very useful in searching of information about not only tourism but also all general domains. Here, we follow the method of conditional random field. This is a machine learning technique based on linguistic rules of Name Entities (NE). It handles nested tagging of name entities with a hierarchical tag set containing forty four attributes (level one), thirty eight attributes (level two) and thirty five attributes (level three) in tag set. We have experimented building Conditional Random Field (CRF) models by training the noun phrases of the training data and it gives encouraging results.